Country

Indicator

Value type

Values present

Claims on central government, etc. (% GDP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	8.74	-17.9%	68
Albania	21.4	-1.76%	29
United Arab Emirates	-6.09	+9.3%	115
Argentina	23.6	-62.3%	23
Armenia	14.2	+11.6%	46
Antigua & Barbuda	6.64	-30.5%	80
Australia	8.69	-5.53%	71
Austria	18.1	-4.24%	39
Azerbaijan	-6.48	+85.6%	116
Belgium	21.4	-6.74%	30
Benin	1.3	-986%	100
Burkina Faso	4.58	+25.6%	89
Bangladesh	14.8	-3.54%	44
Bulgaria	3.93	+105%	93
Bosnia & Herzegovina	2.65	+78.2%	97
Belize	12.8	-6.6%	53
Bolivia	29.2	+25.8%	17
Brazil	51.1	+0.589%	5
Brunei	26.1	+11.4%	20
Bhutan	8.35	-23.8%	72
Botswana	12.9	+75.9%	52
Chile	22.3	+12.4%	25
China	46	+13.8%	8
Côte d’Ivoire	13.6	+4.78%	49
Colombia	6.1	+9.14%	83
Costa Rica	22.9	-2.08%	24
Cyprus	12	-14.7%	58
Czechia	11.9	+24.5%	59
Djibouti	7.57	+60.1%	75
Dominica	10.4	+52.3%	66
Denmark	-5.72	+35.7%	114
Dominican Republic	25.7	+11.2%	21
Algeria	36.9	+15.6%	12
Ecuador	4.31	-12.2%	91
Egypt	55.7	-5.63%	3
Spain	37.2	-4.96%	11
Estonia	-2.87	+7.57%	109
Finland	12.9	-7.5%	51
Fiji	41.3	-7.33%	9
France	29	-0.66%	18
United Kingdom	22.2	-12.1%	26
Georgia	4.94	+55.8%	88
Guinea-Bissau	13.4	+19.1%	50
Greece	19.8	-3.97%	33
Grenada	-24.6	+41%	127
Guatemala	6.75	-13.2%	79
Guyana	12.3	-8.5%	56
Hong Kong SAR China	-21.1	-24.9%	124
Honduras	3.69	-22.6%	94
Croatia	8.74	-10.3%	69
Haiti	9.38	-17.4%	67
Hungary	17.1	+9.68%	42
Indonesia	11.7	-5.28%	62
Ireland	8.3	-3.02%	73
Iraq	7.05	+165%	78
Iceland	-2.66	-12.7%	108
Italy	51.1	-4.94%	6
Jamaica	10.5	-7.61%	65
Jordan	29.7	-1.12%	16
Japan	138	-4.79%	1
Kazakhstan	12.2	+9.58%	57
Kyrgyzstan	-2.1	-38.6%	106
Cambodia	-12.7	+2.87%	119
St. Kitts & Nevis	-26.2	-29.3%	128
Kuwait	-21.9	+0.341%	125
Libya	-8.53	+142%	117
St. Lucia	-11.9	+29.5%	118
Lesotho	-19.1	+78.8%	122
Lithuania	3.97	-18.4%	92
Luxembourg	-2.4	+66.3%	107
Latvia	3.57	-47.1%	95
Macao SAR China	-72.2	+0.854%	130
Morocco	22.1	-51.4%	27
Moldova	2.16	-16.1%	98
Madagascar	5.79	+19.7%	84
Maldives	57.9	+1.59%	2
Mexico	25.4	+14.7%	22
North Macedonia	19.4	+5.15%	35
Mali	11.3	-7.24%	63
Malta	14.3	+6.98%	45
Montenegro	0.656	-90.3%	102
Mozambique	18.5	+39.6%	36
Mauritius	31.6	-6.77%	14
Malaysia	18.5	-1.62%	37
Namibia	11.8	-5.12%	60
Niger	5.54	+8.72%	86
Nicaragua	-3.29	+54.9%	110
Netherlands	11.8	-14.2%	61
Norway	-15.6	+4.98%	121
Nepal	21.7	+2.43%	28
New Zealand	7.4	-44.8%	77
Pakistan	33.5	-5.18%	13
Philippines	30.2	-1.2%	15
Poland	12.6	+3.76%	54
Portugal	28.7	-9.3%	19
Paraguay	-5.37	-4.13%	112
Palestinian Territories	17.7	+55.9%	41
Romania	6.34	+25.4%	82
Rwanda	-1.91	-650%	105
Senegal	18.4	-0.0284%	38
Solomon Islands	0.735	+1,143%	101
Sierra Leone	15.4	+7.05%	43
El Salvador	47.5	+0.982%	7
Somalia	-0.00003	+6.8%	103
Serbia	-0.281	-55.8%	104
Suriname	-3.93	+9.19%	111
Slovakia	20.9	-7.61%	31
Slovenia	13.9	-5.98%	48
Sweden	5.03	-15.5%	87
Eswatini	1.92	-36.9%	99
Seychelles	11.3	-28.7%	64
Togo	7.52	+92.5%	76
Thailand	40.9	+12.9%	10
Timor-Leste	-39.6	-2.7%	129
Tonga	-23.7	-3.67%	126
Trinidad & Tobago	8.28	-16.7%	74
Tunisia	20.2	+20.6%	32
Turkey	14.2	-5.71%	47
Tanzania	5.6	-7.18%	85
Uganda	19.6	+12.3%	34
Ukraine	17.8	-11.6%	40
Uruguay	6.58	-77.4%	81
United States	52.1	+4.48%	4
Uzbekistan	-14.4	-21.4%	120
St. Vincent & Grenadines	12.5	+222%	55
Vanuatu	-5.41	-27.6%	113
Samoa	-19.3	+15.6%	123
Kosovo	3.45	-9.77%	96
South Africa	4.34	-79.9%	90
Zambia	8.7	-23.3%	70

Python
R

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'FS.AST.CGOV.GD.ZS'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)

                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'FS.AST.CGOV.GD.ZS'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))

Compare

Claims on central government, etc. (% GDP)

Year: 2024

Acquire full dataset with Python or R from source